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Cited by 393

The DOI matches its Crossref record; 14 of the 14 references it cites were found in a registry or an index.

Artificial Synapses with Short- and Long-Term Memory for Spiking Neural Networks Based on Renewable Materials

Park, Youngjun, Lee, Jang-Sik·2017·ACS Nano

Abstract

Emulation of biological synapses that perform memory and learning functions is an essential step toward realization of bioinspired neuromorphic systems. Artificial synaptic devices have been developed based mostly on inorganic materials and conventional semiconductor device fabrication processes. Here, we propose flexible biomemristor devices based on lignin by a simple solution process. Lignin is one of the most abundant organic polymers on Earth and is biocompatible, biodegradable, as well as environmentally benign. This memristor emulates several essential synaptic behaviors, including analog memory switching, short-term plasticity, long-term plasticity, spike-rate-dependent plasticity, and short-term to long-term transition. A flexible lignin-based artificial synapse device can be operated without noticeable degradation under mechanical bending test. These results suggest lignin can be a promising key component for artificial synapses and flexible electronic devices.

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Metadata

Authors
Park, Y., & Lee, J.-S.
Venue
ACS Nano
Published
2017-08-28
Volume
11
Issue
9
Pages
8962-8969
ISSN
  • 1936-0851 (ISSN-L)
  • 1936-086X
OpenAlex
W2749019517
Cited by
393
References
14
FWCI
15.44
Type
journal article
Language
en
Open access
closed

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